Methods and systems for orienting a thrust propulsor in response to a failure event of a vertical take-off and landing aircraft

ABSTRACT

Aspects relate to systems and methods for orienting a thrust propulsor in response to a failure event of a vertical take-off and landing (VTOL) aircraft. An exemplary system includes a plurality of lift propulsors mechanically connected to a VTOL aircraft, wherein each of the plurality of lift propulsors are configured to produce lift, a plurality of sensors, wherein at least a sensor is configured to detect a failure of at least a lift propulsor, and transmit a failure datum, a thrust propulsor mechanically attached to the VTOL aircraft with an orientable joint, wherein the thrust propulsor is configured to produce thrust and orient the thrust propulsor as a function of a thrust orientation datum, and a flight controller configured to receive the failure datum, generate a thrust orientation datum as a function of the failure datum, and transmit the thrust orientation datum to the orientable joint.

FIELD OF THE INVENTION

The present invention generally relates to the field of ComputerizedVehicle Controls and Navigation, Radio Wave, Optical and Acoustic WaveCommunication, Robotics, and Nuclear Systems. In particular, the presentinvention is directed to methods and system for orienting a thrustpropulsor in response to a failure event of a vertical take-off andlanding aircraft.

BACKGROUND

Currently commercial aircraft flight is one of the safest modes of humantransport. Unfortunately, it is also one of the greatest contributors togreenhouse gas generation and global climate change. A new paradigm ofhuman flight is fast approaching, one that includes vertical take offand landing vehicles electrically powered through renewable energysources. However, currently these new aircraft lack many of the safetyfeatures currently present on commercial aircraft.

SUMMARY OF THE DISCLOSURE

In an aspect a system for orienting a thrust propulsor in response to afailure event of a vertical take-off and landing aircraft includes aplurality of lift propulsors mechanically connected to a verticaltake-off and landing (VTOL) aircraft, wherein each of the plurality oflift propulsors are configured to produce lift, a plurality of sensors,wherein at least a sensor of the plurality of sensors is configured todetect a failure of at least a lift propulsor of the plurality of liftpropulsors, and transmit a failure datum in response to the failure, athrust propulsor mechanically attached to the VTOL aircraft with anorientable joint, wherein the thrust propulsor is configured to producethrust, and a flight controller communicative with the plurality ofsensors and the orientable joint, wherein the flight controller isconfigured to receive the failure datum, generate a thrust orientationdatum as a function of the failure datum, and transmit the thrustorientation datum to the orientable joint, wherein the orientable jointis configured to receive the thrust orientation datum and orient thethrust propulsor as a function of the thrust orientation datum.

In another aspect a method of orienting a thrust propulsor in responseto a failure event of a vertical take-off and landing aircraft includesproducing, a plurality of lift propulsors mechanically connected to avertical take-off and landing (VTOL) aircraft, lift, detecting, using atleast a sensor of a plurality of sensors, a failure of at least a liftpropulsor of the plurality of lift propulsors, transmitting, using theat least a sensor, a failure datum in response to the failure,producing, using a thrust propulsor mechanically attached to the VTOLaircraft with an orientable joint, thrust, receiving, using a flightcontroller communicative with the plurality of sensors and theorientable joint, the failure datum, generating, using the flightcontroller, a thrust orientation datum as a function of the failuredatum, transmitting, using the flight controller, the thrust orientationdatum to the orientable joint, receiving, using the orientable joint,the thrust orientation datum, and orienting, using the orientable joint,the thrust propulsor as a function of the thrust orientation datum.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram that illustrates an exemplary system fororienting a thrust propulsor in response to a failure event of avertical take-off and landing aircraft;

FIG. 2 is a schematic of an exemplary vertical take-off and landingaircraft;

FIG. 3 is a diagram illustrating exemplary lift and thrust propulsors;

FIG. 4 is a block diagram of an exemplary flight controller;

FIG. 5 is a block diagram illustrating an exemplary machine-learningprocess;

FIG. 6 is a flow diagram illustrating an exemplary method of orienting athrust propulsor in response to a failure event of a vertical take-offand landing aircraft; and

FIG. 7 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for orienting a thrust propulsor in response to afailure event of a vertical take-off and landing (VTOL) aircraft. In anembodiment, VTOL aircraft may include a plurality of lift propulsorsresponsible for generating lift and failure event may include failure ofat least one of those propulsors.

Aspects of the present disclosure can be used to provide a VTOL with yawcontrol after failure of at least a lift propulsor. Aspects of thepresent disclosure can also be used to counter uncontrolled yaw rotationafter failure of at least a lift propulsor. This is so, at least inpart, because loss of a lift propulsor in a multirotor VTOL may resultin uncontrolled yaw rotation in order to stay airborne.

Aspects of the present disclosure allow for controlled flight afterfailure of at least a lift propulsor in a multirotor VTOL. Exemplaryembodiments illustrating aspects of the present disclosure are describedbelow in the context of several specific examples.

Referring now to FIG. 1, an exemplary embodiment of a system 100 fororienting a thrust propulsor in response to a failure event of avertical take-off and landing aircraft is illustrated. System includes aflight controller 104. Flight controller 104 may include any computingdevice as described in this disclosure, including without limitation amicrocontroller, microprocessor, digital signal processor (DSP) and/orsystem on a chip (SoC) as described in this disclosure. Computing devicemay include, be included in, and/or communicate with a mobile devicesuch as a mobile telephone or smartphone. Flight controller 104 mayinclude a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. Flight controller 104 may interface or communicate with one ormore additional devices as described below in further detail via anetwork interface device. Network interface device may be utilized forconnecting flight controller 104 to one or more of a variety ofnetworks, and one or more devices. Examples of a network interfacedevice include, but are not limited to, a network interface card (e.g.,a mobile network interface card, a LAN card), a modem, and anycombination thereof. Examples of a network include, but are not limitedto, a wide area network (e.g., the Internet, an enterprise network), alocal area network (e.g., a network associated with an office, abuilding, a campus or other relatively small geographic space), atelephone network, a data network associated with a telephone/voiceprovider (e.g., a mobile communications provider data and/or voicenetwork), a direct connection between two computing devices, and anycombinations thereof. A network may employ a wired and/or a wirelessmode of communication. In general, any network topology may be used.Information (e.g., data, software etc.) may be communicated to and/orfrom a computer and/or a computing device. Flight controller 104 mayinclude but is not limited to, for example, a computing device orcluster of computing devices in a first location and a second computingdevice or cluster of computing devices in a second location. Flightcontroller 104 may include one or more computing devices dedicated todata storage, security, distribution of traffic for load balancing, andthe like. Flight controller 104 may distribute one or more computingtasks as described below across a plurality of computing devices ofcomputing device, which may operate in parallel, in series, redundantly,or in any other manner used for distribution of tasks or memory betweencomputing devices. Flight controller 104 may be implemented using a“shared nothing” architecture in which data is cached at the worker, inan embodiment, this may enable scalability of system 100 and/orcomputing device.

With continued reference to FIG. 1, flight controller 104 may bedesigned and/or configured to perform any method, method step, orsequence of method steps in any embodiment described in this disclosure,in any order and with any degree of repetition. For instance, flightcontroller 104 may be configured to perform a single step or sequencerepeatedly until a desired or commanded outcome is achieved; repetitionof a step or a sequence of steps may be performed iteratively and/orrecursively using outputs of previous repetitions as inputs tosubsequent repetitions, aggregating inputs and/or outputs of repetitionsto produce an aggregate result, reduction or decrement of one or morevariables such as global variables, and/or division of a largerprocessing task into a set of iteratively addressed smaller processingtasks. Flight controller 104 may perform any step or sequence of stepsas described in this disclosure in parallel, such as simultaneouslyand/or substantially simultaneously performing a step two or more timesusing two or more parallel threads, processor cores, or the like;division of tasks between parallel threads and/or processes may beperformed according to any protocol suitable for division of tasksbetween iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

With continued reference to FIG. 1, system 100 may include a pluralityof lift propulsors 108 a-b mechanically connected to a vertical take-offand landing (VTOL) aircraft. System 100 may include a first liftpropulsor 108 a and a second lift propulsor 108 b. In some cases, eachlift propulsor of plurality of lift propulsors 108 a-b are configured toproduce lift. As used in this disclosure, a “lift propulsor” is apropulsor that may be configured to produce lift, such that during atleast a flight more, for instance without limitation take-off and/orlanding, VTOL aircraft is airborne substantially as a result of liftproduced by one or more lift propulsors. Lift propulsors 108 a-b mayinclude any propulsor described in this disclosure, including withreference to FIG. 2.

With continued reference to FIG. 1, system 100 may include a pluralityof sensors 112 a-b. As used in this disclosure, a sensor is any deviceconfigured to detect a characteristic of the world. System 100 mayinclude a first sensor 112 a and a second sensor 112 b. Plurality ofsensors 112 a-b may include any sensor described in this disclosure,including with reference to FIG. 2. In some cases, a sensor of pluralityof sensors 112 a-b may be configured to detect a failure of at least alift propulsor 108 a-b. As used in this disclosure, a “failure” of alift propulsor is any detectable condition or state that results in adegradation of performance of the lift propulsor. In some cases, atleast a sensor 112 a-b may be configured to transmit a failure datum inresponse to failure of at least a propulsor 108 a-b. As used in thisdisclosure, a “failure datum” is at least an element of data thatrepresents, identifies, describes or otherwise indicates a failure. Forexample in some embodiments, a failure datum may indicate a presence ofa failure. Alternatively or additionally, failure datum may indicateeffects of a failure, for example without limitation by indicatingaffected lift propulsor, describing degradation of propulsorperformance, and the like. According to some embodiments, failure of atleast a lift propulsor 108 a-b may be performed inferred from an sensedattitude (e.g., pitch, roll, yaw) of aircraft. For example, in somecases, a failure of a lift propulsor may cause aircraft to tilt, with acorner at failed propulsor dipping. In some cases, this tilt may bedetected, for instance without limitation by at least an inertialmeasurement unit, and failure datum may indicate this occurrence. Insome embodiments, failure datum may include at least an element of datadescribing failure of at least a lift propulsor 108 a-b. In someembodiments, at least a sensor 112 a-b of plurality of sensors 112 a-bmay include at least a rotation sensor. A rotation sensor may includeany of a Hall effect sensor, an optical rotation sensor, a magneticrotation sensor, and the like. In some cases, rotation sensor 112 a-bmay be configured to detect rotation and/or absence of rotation of atleast a propulsor 108 a-b. In some cases, rotation sensor 112 a-b maysense a rotational speed of at least a propulsor 108 a-b. Alternativelyor additionally, rotational sensor 112 a-b may be configured to detect arotational position of at least a propulsor 108 a-b. In a non-limitingexemplary embodiment, at least a lift propulsor 108 a-b may include arotating shaft and rotation sensor 112 a-b may be within sensedcommunication with rotating shaft. As used in this disclosure, “sensedcommunication” refers to a relationship between two relata, often acomponent being sensed and a sensor, wherein at least a characteristicof one relata may be sensed, measured, or otherwise detected by a secondrelata. In some embodiments, failure of at least a lift propulsor 108a-b introduces a yaw moment. For example, in some cases where VTOLaircraft includes a multirotor aircraft, such as a quadrotor, andfailure of a lift propulsor 108 a may result in a yaw moment, which ifleft unresisted will cause aircraft to experience a yaw rotation.Alternatively or additionally, in some embodiments, failure of at leasta lift propulsor 108 a-b prevents flight controller 104 from usingremaining plurality of lift propulsors to control yaw. For example,failure of a lift propulsor 108 a may not result in a yaw moment but mayresult in a condition where remaining lift propulsors 108 b can nolonger be used to control yaw movement of VTOL aircraft. In someembodiments, failure of at least a lift propulsor 108 a-b degrades liftprovided by plurality of lift propulsors 108 a-b.

With continued reference to FIG. 1, system 100 may include a thrustpropulsor 116. Thrust propulsor 116 may include any propulsor describedin this disclosure, including with reference to FIG. 2. Thrust propulsormay be configured to produce thrust. As used in this disclosure, a“thrust propulsor” is configured to produce a thrust, for example aforward thrust. In some cases, a thrust propulsor may include a pusherpropulsor and/or a pusher propeller. Alternatively or additionally, athrust propulsor may include a puller propulsor and/or a pullerpropeller. Thrust propulsor may be mechanically attached to VTOLaircraft with an orientable joint 120. As used in this disclosure, an“orientable joint” is an attachment mechanism that attaches at least twocomponents to one another and allows for different orientation of thetwo components relative one another. In some cases, an orientable jointmay be controllable by at least an actuator. For example, orientation oftwo components attached by an orientable joint may be changed by atleast an actuator. In some cases, at least an actuator may be manuallycontrolled, for example through linkages, hydraulics, and/or pneumatics.Alternatively and/or additionally, in some cases, at least an actuatormay be controlled automatically, for example by way of a flightcontroller. In some cases, at least an actuator may include anelectro-mechanism. In some embodiments, orientable joint may include agimbal. As used in this disclosure, a “gimbal” is a joint having a pivotto facilitate rotation about at least one axis. In some cases, a gimbalmay allow for rotation about more than one axes, for example withoutlimitation two or three orthogonal axes. In some cases, an orientablejoint may include one or more bearing surfaces. For example withoutlimitation, orientable joint may include one or more of a rollerbearing, a ball bearing, a needle bearing, and the like. In some case,orientable joint may include a linear stage configured to facilitatetranslation. A linear stage may include a linear rail and a carriage. Insome cases, an orientable joint may include a one or more linearactuators, for example a pneumatic and/or hydraulic cylinder.Alternatively or additionally, an orientable joint may include one ormore electric motors configured to facilitate motion, either rotationalor translational. In some embodiments, orientable joint may be locatedsubstantially along a driveshaft between a motor and a propellor; inthis case, the orientable joint may include a universal joint, such aswithout limitation a Cardan joint, a double Cardan joint, or the like.In another embodiment, thrust propulsor may include a jet and or rocket;in this case orientable joint may include one or more systems and/ormechanism for directing thrust of the jet and/or rocket. One or moreelectric motors may be operatively coupled to one or more drivetrains tohelp facilitate motion. One or more drivetrains may include one or moreof a gear, a pulley, a belt, a driveshaft, and the like. In some cases,orientable joint may include at least a sensor which is configured todetect motion and/or orientation of the orientable joint. For example,in some cases, orientable joint 120 may be configured to rotate at arotation sensitive sensor (e.g., Hall effect sensor, magnetic rotationsensor, optical rotation sensor, or the like) may be used to senserotation of orientable joint 120 and/or thrust propulsor 116. In somecases, orientable joint 120 may be further configured to orient inresponse to a detected position or motion of the orientable joint, forexample in a feedback-controlled closed loop. In some cases,feedback-controlled closed loop control of orientable joint 120 mayensure correct orientation of thrust propulsor 116. Alternatively oradditionally, in some embodiments, failure of at least a lift propulsor108 a-b need not occur. In some cases, an extraordinary circumstance,such as without limitation an asymmetric loading of aircraft, may causesystem 100 to re-orient thrust propulsor, without failure of at least alift propulsor 108 a-b. In some additional embodiments, system 100 mayorient thrust propulsor 116 to introduce yaw under normal flightcircumstances.

With continued reference to FIG. 1, flight controller 104 may becommunicative with plurality of sensors 112 a-b and orientable joint120. Flight controller may include any flight controller described inthis disclosure, including with reference to FIG. 4. Flight controller104 may be communicative with at least a sensor 112 a-b and/ororientable joint 120 by way of any communication process described inthis disclosure, including with reference in FIGS. 2-7, for examplewithout limitation by way of a network, such as a controller areanetwork, and or a wired or wireless communication connection. Flightcontroller 104 may receive failure datum from at least a sensor 112 a-b.Flight controller may generate a thrust orientation datum as a functionof failure datum. As used in this disclosure, a “thrust orientationdatum” is an element of data representation an orientation and/ordirection of a thrust propulsor. In some cases, orientable joint isconfigured to orient thrust propulsor according to thrust orientationdatum. Thrust orientation datum may be determined according to any knownflight control methods and/or algorithms, including without limitationproportional control, proportional integrative control, and proportionalintegrative derivative control. Alternatively or additionally thrustorientation datum may be determined according to a thrust model. Forinstance, a thrust model may be used to estimate an amount of forcegenerated by thrust propulsor 116; an estimated required yaw torque maythen be used to calculate a need orientable joint orientation. Flightcontroller 104 may transmit thrust orientation datum to orientable joint120. In some cases, thrust orientation datum may be generated accordingto any flight control algorithm described in this disclosure. Forexample, in some cases, thrust orientation datum may be generated by useof a look-up table. Alternatively or additionally, thrust orientationdatum may be generated in response to at least a pilot control 124input. At least a pilot control 124 may include any pilot controldescribed in this disclosure, including with reference to FIG. 4 below.

With continued reference to FIG. 1, orientable joint 120 may receivethrust orientation datum. Orientable joint 120 may orient thrustpropulsor 116 as a function of thrust orientation datum. In someembodiments, thrust orientation datum may include a rotation; andorientable joint 120 rotates thrust propulsor 116 as a function of therotation of the thrust orientation datum. In some embodiments, thrustorientation datum may include a translation; and orientable joint 120may translate thrust propulsor 120 as a function of the translation ofthe thrust orientation datum. In some embodiments, orienting thrustpropulsor 116 with orientable joint 120 may introduce a yaw moment. Asused in this disclosure, a “yaw moment” is a moment or torque that has acomponent substantially about a yaw axis, for example a yaw axis of VTOLaircraft and/or yaw axis of thrust propulsor. In some cases, yaw momentmay be controlled, for example by flight controller 104 by way of thrustorientation datum, to control yaw of the VTOL aircraft.

Referring now to FIG. 2, an exemplary embodiment of an aircraft 200 isillustrated. Aircraft 200 may include a vertical takeoff and landing(VTOL) aircraft. In some cases, VTOL may by electrically powered and mayinclude an electric vertical take-off and landing aircraft (eVTOL). VTOLmay be capable of rotor-based cruising flight, rotor-based takeoff,rotor-based landing, fixed-wing cruising flight, airplane-style takeoff,airplane-style landing, and/or any combination thereof. “Rotor-basedflight,” as described in this disclosure, is where the aircraftgenerated lift and propulsion by way of one or more powered rotorscoupled with an engine, such as a quadcopter, multi-rotor helicopter, orother vehicle that maintains its lift primarily using downward thrustingpropulsors. “Fixed-wing flight,” as described in this disclosure, iswhere the aircraft is capable of flight using wings and/or foils thatgenerate lift caused by the aircraft's forward airspeed and the shape ofthe wings and/or foils, such as airplane-style flight.

Still referring to FIG. 2, aircraft 200 may include a fuselage 204. Asused in this disclosure a “fuselage” is the main body of an aircraft, orin other words, the entirety of the aircraft except for the cockpit,nose, wings, empennage, nacelles, any and all control surfaces, andgenerally contains an aircraft's payload. Fuselage 204 may comprisestructural elements that physically support the shape and structure ofan aircraft. Structural elements may take a plurality of forms, alone orin combination with other types. Structural elements may vary dependingon the construction type of aircraft and specifically, the fuselage.Fuselage 204 may comprise a truss structure. A truss structure may beused with a lightweight aircraft and may include welded aluminum tubetrusses. A truss, as used herein, is an assembly of beams that create arigid structure, often in combinations of triangles to createthree-dimensional shapes. A truss structure may alternatively comprisetitanium construction in place of aluminum tubes, or a combinationthereof In some embodiments, structural elements may comprise aluminumtubes and/or titanium beams. In an embodiment, and without limitation,structural elements may include an aircraft skin. Aircraft skin may belayered over the body shape constructed by trusses. Aircraft skin maycomprise a plurality of materials such as aluminum, fiberglass, and/orcarbon fiber, the latter of which will be addressed in greater detaillater in this paper.

Still referring to FIG. 2, aircraft 200 may include a plurality ofactuators 208. Actuator 208 may include any actuator described in thisdisclosure, for instance in reference to FIG. 1. In an embodiment,actuator 208 may be mechanically coupled to an aircraft. As used herein,a person of ordinary skill in the art would understand “mechanicallycoupled” to mean that at least a portion of a device, component, orcircuit is connected to at least a portion of the aircraft via amechanical coupling. Said mechanical coupling can include, for example,rigid coupling, such as beam coupling, bellows coupling, bushed pincoupling, constant velocity, split-muff coupling, diaphragm coupling,disc coupling, donut coupling, elastic coupling, flexible coupling,fluid coupling, gear coupling, grid coupling, Hirth joints, hydrodynamiccoupling, jaw coupling, magnetic coupling, Oldham coupling, sleevecoupling, tapered shaft lock, twin spring coupling, rag joint coupling,universal joints, or any combination thereof. As used in this disclosurean “aircraft” is vehicle that may fly. As a non-limiting example,aircraft may include airplanes, helicopters, airships, blimps, gliders,paramotors, and the like thereof. In an embodiment, mechanical couplingmay be used to connect the ends of adjacent parts and/or objects of anelectric aircraft. Further, in an embodiment, mechanical coupling may beused to join two pieces of rotating electric aircraft components.

With continued reference to FIG. 2, a plurality of actuators 208 may beconfigured to produce a torque. As used in this disclosure a “torque” isa measure of force that causes an object to rotate about an axis in adirection. For example, and without limitation, torque may rotate anaileron and/or rudder to generate a force that may adjust and/or affectaltitude, airspeed velocity, groundspeed velocity, direction duringflight, and/or thrust. For example, plurality of actuators 208 mayinclude a component used to produce a torque that affects aircrafts'roll and pitch, such as without limitation one or more ailerons. An“aileron,” as used in this disclosure, is a hinged surface which formpart of the trailing edge of a wing in a fixed wing aircraft, and whichmay be moved via mechanical means such as without limitationservomotors, mechanical linkages, or the like. As a further example,plurality of actuators 208 may include a rudder, which may include,without limitation, a segmented rudder that produces a torque about avertical axis. Additionally or alternatively, plurality of actuators 208may include other flight control surfaces such as propulsors, rotatingflight controls, or any other structural features which can adjustmovement of aircraft 200. Plurality of actuators 208 may include one ormore rotors, turbines, ducted fans, paddle wheels, and/or othercomponents configured to propel a vehicle through a fluid mediumincluding, but not limited to air.

Still referring to FIG. 2, plurality of actuators 208 may include atleast a propulsor. As used in this disclosure a “propulsor” is acomponent and/or device used to propel a craft by exerting force on afluid medium, which may include a gaseous medium such as air or a liquidmedium such as water. In an embodiment, when a propulsor twists andpulls air behind it, it may, at the same time, push an aircraft forwardwith an amount of force and/or thrust. More air pulled behind anaircraft results in greater thrust with which the aircraft is pushedforward. Propulsor component may include any device or component thatconsumes electrical power on demand to propel an electric aircraft in adirection or other vehicle while on ground or in-flight. In anembodiment, propulsor component may include a puller component. As usedin this disclosure a “puller component” is a component that pulls and/ortows an aircraft through a medium. As a non-limiting example, pullercomponent may include a flight component such as a puller propeller, apuller motor, a puller propulsor, and the like. Additionally, oralternatively, puller component may include a plurality of puller flightcomponents. In another embodiment, propulsor component may include apusher component. As used in this disclosure a “pusher component” is acomponent that pushes and/or thrusts an aircraft through a medium. As anon-limiting example, pusher component may include a pusher componentsuch as a pusher propeller, a pusher motor, a pusher propulsor, and thelike. Additionally, or alternatively, pusher flight component mayinclude a plurality of pusher flight components.

In another embodiment, and still referring to FIG. 2, propulsor mayinclude a propeller, a blade, or any combination of the two. A propellermay function to convert rotary motion from an engine or other powersource into a swirling slipstream which may push the propeller forwardsor backwards. Propulsor may include a rotating power-driven hub, towhich several radial airfoil-section blades may be attached, such thatan entire whole assembly rotates about a longitudinal axis. As anon-limiting example, blade pitch of propellers may be fixed at a fixedangle, manually variable to a few set positions, automatically variable(e.g. a “constant-speed” type), and/or any combination thereof asdescribed further in this disclosure. As used in this disclosure a“fixed angle” is an angle that is secured and/or substantially unmovablefrom an attachment point. For example, and without limitation, a fixedangle may be an angle of 2.2° inward and/or 1.7° forward. As a furthernon-limiting example, a fixed angle may be an angle of 3.6° outwardand/or 2.7° backward. In an embodiment, propellers for an aircraft maybe designed to be fixed to their hub at an angle similar to the threadon a screw makes an angle to the shaft; this angle may be referred to asa pitch or pitch angle which may determine a speed of forward movementas the blade rotates. Additionally or alternatively, propulsor componentmay be configured having a variable pitch angle. As used in thisdisclosure a “variable pitch angle” is an angle that may be moved and/orrotated. For example, and without limitation, propulsor component may beangled at a first angle of 3.3° inward, wherein propulsor component maybe rotated and/or shifted to a second angle of 1.7° outward.

Still referring to FIG. 2, propulsor may include a thrust element whichmay be integrated into the propulsor. Thrust element may include,without limitation, a device using moving or rotating foils, such as oneor more rotors, an airscrew or propeller, a set of airscrews orpropellers such as contra-rotating propellers, a moving or flappingwing, or the like. Further, a thrust element, for example, can includewithout limitation a marine propeller or screw, an impeller, a turbine,a pump-jet, a paddle or paddle-based device, or the like.

With continued reference to FIG. 2, plurality of actuators 208 mayinclude power sources, control links to one or more elements, fuses,and/or mechanical couplings used to drive and/or control any otherflight component. Plurality of actuators 208 may include a motor thatoperates to move one or more flight control components and/or one ormore control surfaces, to drive one or more propulsors, or the like. Amotor may be driven by direct current (DC) electric power and mayinclude, without limitation, brushless DC electric motors, switchedreluctance motors, induction motors, or any combination thereof.Alternatively or additionally, a motor may be driven by an inverter. Amotor may also include electronic speed controllers, inverters, or othercomponents for regulating motor speed, rotation direction, and/ordynamic braking.

Still referring to FIG. 2, plurality of actuators 208 may include anenergy source. An energy source may include, for example, a generator, aphotovoltaic device, a fuel cell such as a hydrogen fuel cell, directmethanol fuel cell, and/or solid oxide fuel cell, an electric energystorage device (e.g. a capacitor, an inductor, and/or a battery). Anenergy source may also include a battery cell, or a plurality of batterycells connected in series into a module and each module connected inseries or in parallel with other modules. Configuration of an energysource containing connected modules may be designed to meet an energy orpower requirement and may be designed to fit within a designatedfootprint in an electric aircraft in which system may be incorporated.

In an embodiment, and still referring to FIG. 2, an energy source may beused to provide a steady supply of electrical power to a load over aflight by an electric aircraft 200. For example, energy source may becapable of providing sufficient power for “cruising” and otherrelatively low-energy phases of flight. An energy source may also becapable of providing electrical power for some higher-power phases offlight as well, particularly when the energy source is at a high SOC, asmay be the case for instance during takeoff. In an embodiment, energysource may include an emergency power unit which may be capable ofproviding sufficient electrical power for auxiliary loads includingwithout limitation, lighting, navigation, communications, de-icing,steering or other systems requiring power or energy. Further, energysource may be capable of providing sufficient power for controlleddescent and landing protocols, including, without limitation, hoveringdescent or runway landing. As used herein the energy source may havehigh power density where electrical power an energy source can usefullyproduce per unit of volume and/or mass is relatively high. As used inthis disclosure, “electrical power” is a rate of electrical energy perunit time. An energy source may include a device for which power thatmay be produced per unit of volume and/or mass has been optimized, forinstance at an expense of maximal total specific energy density or powercapacity. Non-limiting examples of items that may be used as at least anenergy source include batteries used for starting applications includingLi ion batteries which may include NCA, NMC, Lithium iron phosphate(LiFePO4) and Lithium Manganese Oxide (LMO) batteries, which may bemixed with another cathode chemistry to provide more specific power ifthe application requires Li metal batteries, which have a lithium metalanode that provides high power on demand, Li ion batteries that have asilicon or titanite anode, energy source may be used, in an embodiment,to provide electrical power to an electric aircraft or drone, such as anelectric aircraft vehicle, during moments requiring high rates of poweroutput, including without limitation takeoff, landing, thermal de-icingand situations requiring greater power output for reasons of stability,such as high turbulence situations, as described in further detailbelow. A battery may include, without limitation a battery using nickelbased chemistries such as nickel cadmium or nickel metal hydride, abattery using lithium ion battery chemistries such as a nickel cobaltaluminum (NCA), nickel manganese cobalt (NMC), lithium iron phosphate(LiFePO4), lithium cobalt oxide (LCO), and/or lithium manganese oxide(LMO), a battery using lithium polymer technology, lead-based batteriessuch as without limitation lead acid batteries, metal-air batteries, orany other suitable battery. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various devices ofcomponents that may be used as an energy source.

Still referring to FIG. 2, an energy source may include a plurality ofenergy sources, referred to herein as a module of energy sources. Modulemay include batteries connected in parallel or in series or a pluralityof modules connected either in series or in parallel designed to satisfyboth power and energy requirements. Connecting batteries in series mayincrease a potential of at least an energy source which may provide morepower on demand. High potential batteries may require cell matching whenhigh peak load is needed. As more cells are connected in strings, theremay exist a possibility of one cell failing which may increaseresistance in module and reduce overall power output as voltage of themodule may decrease as a result of that failing cell. Connectingbatteries in parallel may increase total current capacity by decreasingtotal resistance, and it also may increase overall amp-hour capacity.Overall energy and power outputs of at least an energy source may bebased on individual battery cell performance or an extrapolation basedon a measurement of at least an electrical parameter. In an embodimentwhere energy source includes a plurality of battery cells, overall poweroutput capacity may be dependent on electrical parameters of eachindividual cell. If one cell experiences high self-discharge duringdemand, power drawn from at least an energy source may be decreased toavoid damage to a weakest cell. Energy source may further include,without limitation, wiring, conduit, housing, cooling system and batterymanagement system. Persons skilled in the art will be aware, afterreviewing the entirety of this disclosure, of many different componentsof an energy source. Exemplary energy sources are disclosed in detail inU.S. patent application Ser. Nos. 16/848,157 and 16/048,140 bothentitled “SYSTEM AND METHOD FOR HIGH ENERGY DENSITY BATTERY MODULE” byS. Donovan et al., which are incorporated in their entirety herein byreference.

Still referring to FIG. 2, according to some embodiments, an energysource may include an emergency power unit (EPU) (i.e., auxiliary powerunit). As used in this disclosure an “emergency power unit” is an energysource as described herein that is configured to power an essentialsystem for a critical function in an emergency, for instance withoutlimitation when another energy source has failed, is depleted, or isotherwise unavailable. Exemplary non-limiting essential systems includenavigation systems, such as MFD, GPS, VOR receiver or directional gyro,and other essential flight components, such as propulsors.

Still referring to FIG. 2, another exemplary actuator may includelanding gear. Landing gear may be used for take-off and/orlanding/Landing gear may be used to contact ground while aircraft 200 isnot in flight. Exemplary landing gear is disclosed in detail in U.S.patent application Ser. No. 17/196,619 entitled “SYSTEM FOR ROLLINGLANDING GEAR” by R. Griffin et al., which is incorporated in itsentirety herein by reference.

Still referring to FIG. 2, aircraft 200 may include a pilot control 212,including without limitation, a hover control, a thrust control, aninceptor stick, a cyclic, and/or a collective control. As used in thisdisclosure a “collective control” is a mechanical control of an aircraftthat allows a pilot to adjust and/or control the pitch angle of theplurality of actuators 208. For example and without limitation,collective control may alter and/or adjust the pitch angle of all of themain rotor blades collectively. For example, and without limitationpilot control 212 may include a yoke control. As used in this disclosurea “yoke control” is a mechanical control of an aircraft to control thepitch and/or roll. For example and without limitation, yoke control mayalter and/or adjust the roll angle of aircraft 200 as a function ofcontrolling and/or maneuvering ailerons. In an embodiment, pilot control212 may include one or more foot-brakes, control sticks, pedals,throttle levels, and the like thereof. In another embodiment, andwithout limitation, pilot control 212 may be configured to control aprincipal axis of the aircraft. As used in this disclosure a “principalaxis” is an axis in a body representing one three dimensionalorientations. For example, and without limitation, principal axis ormore yaw, pitch, and/or roll axis. Principal axis may include a yawaxis. As used in this disclosure a “yaw axis” is an axis that isdirected towards the bottom of the aircraft, perpendicular to the wings.For example, and without limitation, a positive yawing motion mayinclude adjusting and/or shifting the nose of aircraft 200 to the right.Principal axis may include a pitch axis. As used in this disclosure a“pitch axis” is an axis that is directed towards the right laterallyextending wing of the aircraft. For example, and without limitation, apositive pitching motion may include adjusting and/or shifting the noseof aircraft 200 upwards. Principal axis may include a roll axis. As usedin this disclosure a “roll axis” is an axis that is directedlongitudinally towards the nose of the aircraft, parallel to thefuselage. For example, and without limitation, a positive rolling motionmay include lifting the left and lowering the right wing concurrently.

Still referring to FIG. 2, pilot control 212 may be configured to modifya variable pitch angle. For example, and without limitation, pilotcontrol 212 may adjust one or more angles of attack of a propeller. Asused in this disclosure an “angle of attack” is an angle between thechord of the propeller and the relative wind. For example, and withoutlimitation angle of attack may include a propeller blade angled 3.2°. Inan embodiment, pilot control 212 may modify the variable pitch anglefrom a first angle of 2.61° to a second angle of 3.72°. Additionally oralternatively, pilot control 212 may be configured to translate a pilotdesired torque for flight component 108. For example, and withoutlimitation, pilot control 212 may translate that a pilot's desiredtorque for a propeller be 150 lb. ft. of torque. As a furthernon-limiting example, pilot control 212 may introduce a pilot's desiredtorque for a propulsor to be 280 lb. ft. of torque. Additionaldisclosure related to pilot control 212 may be found in U.S. patentapplication Ser. Nos. 17/001,845 and 16/829,206 both of which areentitled “A HOVER AND THRUST CONTROL ASSEMBLY FOR DUAL-MODE AIRCRAFT” byC.

Spiegel et al., which are incorporated in their entirety herein byreference.

Still referring to FIG. 2, aircraft 200 may include a loading system. Aloading system may include a system configured to load an aircraft ofeither cargo or personnel. For instance, some exemplary loading systemsmay include a swing nose, which is configured to swing the nose ofaircraft 200 of the way thereby allowing direct access to a cargo baylocated behind the nose. A notable exemplary swing nose aircraft isBoeing 747. Additional disclosure related to loading systems can befound in U.S. patent application Ser. No. 17/137,584 entitled “SYSTEMAND METHOD FOR LOADING AND SECURING PAYLOAD IN AN AIRCRAFT” by R.Griffin et al., entirety of which in incorporated herein by reference.

Still referring to FIG. 2, aircraft 200 may include a sensor 216. Sensor216 may include any sensor described in this disclosure, for instance inreference to FIG. 1. Sensor 216 may be configured to sense acharacteristic of pilot control 212. Sensor may be a device, module,and/or subsystem, utilizing any hardware, software, and/or anycombination thereof to sense a characteristic and/or changes thereof, inan instant environment, for instance without limitation a pilot control212, which the sensor is proximal to or otherwise in a sensedcommunication with, and transmit information associated with thecharacteristic, for instance without limitation digitized data. Sensor216 may be mechanically and/or communicatively coupled to aircraft 200,including, for instance, to at least a pilot control 212. Sensor 216 maybe configured to sense a characteristic associated with at least a pilotcontrol 212. An environmental sensor may include without limitation oneor more sensors used to detect ambient temperature, barometric pressure,and/or air velocity, one or more motion sensors which may includewithout limitation gyroscopes, accelerometers, inertial measurement unit(IMU), and/or magnetic sensors, one or more humidity sensors, one ormore oxygen sensors, or the like. Additionally or alternatively, sensor216 may include at least a geospatial sensor. Sensor 216 may be locatedinside an aircraft; and/or be included in and/or attached to at least aportion of the aircraft. Sensor may include one or more proximitysensors, displacement sensors, vibration sensors, and the like thereof.Sensor may be used to monitor the status of aircraft 200 for bothcritical and non-critical functions. Sensor may be incorporated intovehicle or aircraft or be remote.

Still referring to FIG. 2, in some embodiments, sensor 216 may beconfigured to sense a characteristic associated with any pilot controldescribed in this disclosure. Non-limiting examples of a sensor 216 mayinclude an inertial measurement unit (IMU), an accelerometer, agyroscope, a proximity sensor, a pressure sensor, a light sensor, apitot tube, an air speed sensor, a position sensor, a speed sensor, aswitch, a thermometer, a strain gauge, an acoustic sensor, and anelectrical sensor. In some cases, sensor 216 may sense a characteristicas an analog measurement, for instance, yielding a continuously variableelectrical potential indicative of the sensed characteristic. In thesecases, sensor 216 may additionally comprise an analog to digitalconverter (ADC) as well as any additionally circuitry, such as withoutlimitation a Whetstone bridge, an amplifier, a filter, and the like. Forinstance, in some cases, sensor 216 may comprise a strain gageconfigured to determine loading of one or flight components, forinstance landing gear. Strain gage may be included within a circuitcomprising a Whetstone bridge, an amplified, and a bandpass filter toprovide an analog strain measurement signal having a high signal tonoise ratio, which characterizes strain on a landing gear member. An ADCmay then digitize analog signal produces a digital signal that can thenbe transmitted other systems within aircraft 200, for instance withoutlimitation a computing system, a pilot display, and a memory component.Alternatively or additionally, sensor 216 may sense a characteristic ofa pilot control 212 digitally. For instance in some embodiments, sensor216 may sense a characteristic through a digital means or digitize asensed signal natively. In some cases, for example, sensor 216 mayinclude a rotational encoder and be configured to sense a rotationalposition of a pilot control; in this case, the rotational encoderdigitally may sense rotational “clicks” by any known method, such aswithout limitation magnetically, optically, and the like.

Still referring to FIG. 2, electric aircraft 200 may include at least amotor 224, which may be mounted on a structural feature of the aircraft.Design of motor 224 may enable it to be installed external to structuralmember (such as a boom, nacelle, or fuselage) for easy maintenanceaccess and to minimize accessibility requirements for the structure;this may improve structural efficiency by requiring fewer large holes inthe mounting area. In some embodiments, motor 224 may include two mainholes in top and bottom of mounting area to access bearing cartridge.Further, a structural feature may include a component of electricaircraft 200. For example, and without limitation structural feature maybe any portion of a vehicle incorporating motor 224, including anyvehicle as described in this disclosure. As a further non-limitingexample, a structural feature may include without limitation a wing, aspar, an outrigger, a fuselage, or any portion thereof; persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of many possible features that may function as at least astructural feature. At least a structural feature may be constructed ofany suitable material or combination of materials, including withoutlimitation metal such as aluminum, titanium, steel, or the like, polymermaterials or composites, fiberglass, carbon fiber, wood, or any othersuitable material. As a non-limiting example, at least a structuralfeature may be constructed from additively manufactured polymer materialwith a carbon fiber exterior; aluminum parts or other elements may beenclosed for structural strength, or for purposes of supporting, forinstance, vibration, torque or shear stresses imposed by at leastpropulsor 208. Persons skilled in the art, upon reviewing the entiretyof this disclosure, will be aware of various materials, combinations ofmaterials, and/or constructions techniques.

Still referring to FIG. 2, electric aircraft 200 may include a verticaltakeoff and landing aircraft (VTOL). As used herein, a “verticaltake-off and landing (VTOL) aircraft” is one that can hover, take off,and land vertically. A VTOL, as used herein, is an electrically poweredaircraft typically using an energy source, of a plurality of energysources to power the aircraft. In order to optimize the power and energynecessary to propel the aircraft. VTOL may be capable of rotor-basedcruising flight, rotor-based takeoff, rotor-based landing, fixed-wingcruising flight, airplane-style takeoff, airplane-style landing, and/orany combination thereof. Rotor-based flight, as described herein, iswhere the aircraft generated lift and propulsion by way of one or morepowered rotors coupled with an engine, such as a “quad copter,”multi-rotor helicopter, or other vehicle that maintains its liftprimarily using downward thrusting propulsors. Fixed-wing flight, asdescribed herein, is where the aircraft is capable of flight using wingsand/or foils that generate life caused by the aircraft's forwardairspeed and the shape of the wings and/or foils, such as airplane-styleflight. In some embodiments, a VTOL aircraft 200 includes at least anaircraft component. As used in this disclosure, an “aircraft component”is any part of an aircraft, for example without limitation pilotcontrols, sensors, flight components, propulsors, landing gear, and thelike.

With continued reference to FIG. 2, a number of aerodynamic forces mayact upon the electric aircraft 200 during flight. Forces acting onelectric aircraft 200 during flight may include, without limitation,thrust, the forward force produced by the rotating element of theelectric aircraft 200 and acts parallel to the longitudinal axis.Another force acting upon electric aircraft 200 may be, withoutlimitation, drag, which may be defined as a rearward retarding forcewhich is caused by disruption of airflow by any protruding surface ofthe electric aircraft 200 such as, without limitation, the wing, rotor,and fuselage. Drag may oppose thrust and acts rearward parallel to therelative wind. A further force acting upon electric aircraft 200 mayinclude, without limitation, weight, which may include a combined loadof the electric aircraft 200 itself, crew, baggage, and/or fuel. Weightmay pull electric aircraft 200 downward due to the force of gravity. Anadditional force acting on electric aircraft 200 may include, withoutlimitation, lift, which may act to oppose the downward force of weightand may be produced by the dynamic effect of air acting on the airfoiland/or downward thrust from the propulsor 208 of the electric aircraft.Lift generated by the airfoil may depend on speed of airflow, density ofair, total area of an airfoil and/or segment thereof, and/or an angle ofattack between air and the airfoil. For example, and without limitation,electric aircraft 200 are designed to be as lightweight as possible.Reducing the weight of the aircraft and designing to reduce the numberof components is essential to optimize the weight. To save energy, itmay be useful to reduce weight of components of electric aircraft 200,including without limitation propulsors and/or propulsion assemblies. Inan embodiment, motor 224 may eliminate need for many external structuralfeatures that otherwise might be needed to join one component to anothercomponent. Motor 224 may also increase energy efficiency by enabling alower physical propulsor profile, reducing drag and/or wind resistance.This may also increase durability by lessening the extent to which dragand/or wind resistance add to forces acting on electric aircraft 200and/or propulsors.

With continued reference to FIG. 2, exemplary VTOL aircraft 200 show inFIG. 2 includes four lift propulsors 228. As described above, a liftpropulsor 228 may provide lift to VTOL aircraft 200. exemplary VTOLaircraft 200 is shown with a thrust propulsor 232 configured in a pusherconfiguration to produce a forward thrust force. A coordinate system isshown in FIG. 2 offset from aircraft for clarity. A yaw axis is shownhaving a yaw rotation 236 about it. A pitch axis is shown having a pitchrotation 240 about it. And a roll axis is shown having a roll rotation244. In some cases, coordinate system may be located at a center withinaircraft 200, for example at a center of mass, a centroid, or the like.Alternatively or additionally, in some cases, coordinate system may belocated at a center of an aircraft component, for example an orientablejoint and or a thrust propulsor 232.

With continued reference to FIG. 2, in an exemplary failure, one of fourlift propulsors 228 may fail completely and cease to provide propulsion.In this case, remaining three lift propulsors may 228 be allow VTOLaircraft 200 to remain airborne, but as a result of unbalancedpropulsion and/or operation from only three of four lift propulsors 228,aircraft 200 may be less controllable in one or more directions forexample yaw 236. In some cases, as a result of unbalanced propulsionand/or operation from only three of four lift propulsors 228, aircraft200 may experience an added moment, for example a yaw moment 236.

Referring now to FIG. 3, propulsors of an exemplary vertical take-offand landing (VTOL) aircraft 300 are schematically illustrated. In someembodiments, aircraft 300 may include four lift propulsors 304. Anexemplary lift propulsor 304 is illustrated in FIG. 3 in two viewsshowing cant angle of lift propulsor. Lift propulsor 304 can be seen ina first view 308 to have a left-to-right cant angle (along a planenormal to a roll 144 axis, see FIG. 2) of about 0.6° in (towardaircraft). Lift propulsor 304 can be seen in a second view 312 to have afore-aft cant angle (along a plane normal to a pitch 240 axis, see FIG.2) of about 3° out (away from aircraft). In some cases, all liftpropulsors 304 have substantially similar cant angles. In some cases,cant angles of all propulsors have directional vectors which intersectabout a center of aircraft, for example an aerodynamic center ofaircraft, a center of mass of aircraft, a centroid of aircraft, or thelike. In some cases, directional vectors of lift propulsors when addedtogether result in a single vector that is substantially directedstraight up (along a yaw 236 axis, see FIG. 2). VTOL aircraft 300 mayadditionally include a thrust propulsor 316, for example withoutlimitation a pusher propeller located at rear of the aircraft. FIG. 3shows a thrust propulsor 316 from a view top-down vantage (plane that isorthogonal to yaw 236 axis, see FIG. 2). Thrust propulsor 316 may beconfigured with an orientable joint. In some cases, orientable joint mayallow for rotation of thrust propulsor 316. For example, in someembodiments, thrust propulsor 316 may be rotated +/−15° about a yawaxis. Alternatively or additionally, in some embodiments, thrustpropulsor 316 may be rotated in one or more different axes (e.g., pitchor roll axes or some combination of pitch, roll, and/or yaw axes) and inangles greater or lesser than +/−15°. For example in some cases, thrustpropulsor 316 may in some cases, be rotated up to +/−90° in any axisaccording to some embodiments.

With continued reference to FIG. 3, in some cases, one of four liftpropulsors 304 may fail. In some cases, this failure may be referred toas a corner out failure. In response a lift propulsor opposite failedlift propulsor may be operated differently, for example in reverse, inorder to prevent unintended pitch and/or roll of aircraft 300. In somecases, cant of one or more lift propulsors 304 may be set in order toprevent from a corner out failure from resulting in substantialuncontrolled yaw rotation of aircraft. Alternatively or additionally, insome cases, thrust propulsor 316 may be oriented to control yaw ofaircraft 300 experiencing a corner out failure.

Now referring to FIG. 4, an exemplary embodiment 400 of a flightcontroller 404 is illustrated. As used in this disclosure a “flightcontroller” is a computing device of a plurality of computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and flight instruction. Flight controller 404 may includeand/or communicate with any computing device as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Further, flight controller 404may include a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. In embodiments, flight controller 404 may be installed in anaircraft, may control the aircraft remotely, and/or may include anelement installed in the aircraft and a remote element in communicationtherewith.

In an embodiment, and still referring to FIG. 4, flight controller 404may include a signal transformation component 408. As used in thisdisclosure a “signal transformation component” is a component thattransforms and/or converts a first signal to a second signal, wherein asignal may include one or more digital and/or analog signals. Forexample, and without limitation, signal transformation component 408 maybe configured to perform one or more operations such as preprocessing,lexical analysis, parsing, semantic analysis, and the like thereof. Inan embodiment, and without limitation, signal transformation component408 may include one or more analog-to-digital convertors that transforma first signal of an analog signal to a second signal of a digitalsignal. For example, and without limitation, an analog-to-digitalconverter may convert an analog input signal to a 10-bit binary digitalrepresentation of that signal. In another embodiment, signaltransformation component 408 may include transforming one or morelow-level languages such as, but not limited to, machine languagesand/or assembly languages. For example, and without limitation, signaltransformation component 408 may include transforming a binary languagesignal to an assembly language signal. In an embodiment, and withoutlimitation, signal transformation component 408 may include transformingone or more high-level languages and/or formal languages such as but notlimited to alphabets, strings, and/or languages. For example, andwithout limitation, high-level languages may include one or more systemlanguages, scripting languages, domain-specific languages, visuallanguages, esoteric languages, and the like thereof. As a furthernon-limiting example, high-level languages may include one or morealgebraic formula languages, business data languages, string and listlanguages, object-oriented languages, and the like thereof.

Still referring to FIG. 4, signal transformation component 408 may beconfigured to optimize an intermediate representation 412. As used inthis disclosure an “intermediate representation” is a data structureand/or code that represents the input signal. Signal transformationcomponent 408 may optimize intermediate representation as a function ofa data-flow analysis, dependence analysis, alias analysis, pointeranalysis, escape analysis, and the like thereof. In an embodiment, andwithout limitation, signal transformation component 408 may optimizeintermediate representation 412 as a function of one or more inlineexpansions, dead code eliminations, constant propagation, looptransformations, and/or automatic parallelization functions. In anotherembodiment, signal transformation component 408 may optimizeintermediate representation as a function of a machine dependentoptimization such as a peephole optimization, wherein a peepholeoptimization may rewrite short sequences of code into more efficientsequences of code. Signal transformation component 408 may optimizeintermediate representation to generate an output language, wherein an“output language,” as used herein, is the native machine language offlight controller 404. For example, and without limitation, nativemachine language may include one or more binary and/or numericallanguages.

In an embodiment, and without limitation, signal transformationcomponent 408 may include transform one or more inputs and outputs as afunction of an error correction code. An error correction code, alsoknown as error correcting code (ECC), is an encoding of a message or lotof data using redundant information, permitting recovery of corrupteddata. An ECC may include a block code, in which information is encodedon fixed-size packets and/or blocks of data elements such as symbols ofpredetermined size, bits, or the like. Reed-Solomon coding, in whichmessage symbols within a symbol set having q symbols are encoded ascoefficients of a polynomial of degree less than or equal to a naturalnumber k, over a finite field F with q elements; strings so encoded havea minimum hamming distance of k+1, and permit correction of (q−k−1)/2erroneous symbols. Block code may alternatively or additionally beimplemented using Golay coding, also known as binary Golay coding,Bose-Chaudhuri, Hocquenghuem (BCH) coding, multidimensional parity-checkcoding, and/or Hamming codes. An ECC may alternatively or additionallybe based on a convolutional code.

In an embodiment, and still referring to FIG. 4, flight controller 404may include a reconfigurable hardware platform 416. A “reconfigurablehardware platform,” as used herein, is a component and/or unit ofhardware that may be reprogrammed, such that, for instance, a data pathbetween elements such as logic gates or other digital circuit elementsmay be modified to change an algorithm, state, logical sequence, or thelike of the component and/or unit. This may be accomplished with suchflexible high-speed computing fabrics as field-programmable gate arrays(FPGAs), which may include a grid of interconnected logic gates,connections between which may be severed and/or restored to program inmodified logic. Reconfigurable hardware platform 416 may be reconfiguredto enact any algorithm and/or algorithm selection process received fromanother computing device and/or created using machine-learningprocesses.

Still referring to FIG. 4, reconfigurable hardware platform 416 mayinclude a logic component 420. As used in this disclosure a “logiccomponent” is a component that executes instructions on output language.For example, and without limitation, logic component may perform basicarithmetic, logic, controlling, input/output operations, and the likethereof. Logic component 420 may include any suitable processor, such aswithout limitation a component incorporating logical circuitry forperforming arithmetic and logical operations, such as an arithmetic andlogic unit (ALU), which may be regulated with a state machine anddirected by operational inputs from memory and/or sensors; logiccomponent 420 may be organized according to Von Neumann and/or Harvardarchitecture as a non-limiting example. Logic component 420 may include,incorporate, and/or be incorporated in, without limitation, amicrocontroller, microprocessor, digital signal processor (DSP), FieldProgrammable Gate Array (FPGA), Complex Programmable Logic Device(CPLD), Graphical Processing Unit (GPU), general purpose GPU, TensorProcessing Unit (TPU), analog or mixed signal processor, TrustedPlatform Module (TPM), a floating-point unit (FPU), and/or system on achip (SoC). In an embodiment, logic component 420 may include one ormore integrated circuit microprocessors, which may contain one or morecentral processing units, central processors, and/or main processors, ona single metal-oxide-semiconductor chip. Logic component 420 may beconfigured to execute a sequence of stored instructions to be performedon the output language and/or intermediate representation 412. Logiccomponent 420 may be configured to fetch and/or retrieve the instructionfrom a memory cache, wherein a “memory cache,” as used in thisdisclosure, is a stored instruction set on flight controller 404. Logiccomponent 420 may be configured to decode the instruction retrieved fromthe memory cache to opcodes and/or operands. Logic component 420 may beconfigured to execute the instruction on intermediate representation 412and/or output language. For example, and without limitation, logiccomponent 420 may be configured to execute an addition operation onintermediate representation 412 and/or output language.

In an embodiment, and without limitation, logic component 420 may beconfigured to calculate a flight element 424. As used in this disclosurea “flight element” is an element of datum denoting a relative status ofaircraft. For example, and without limitation, flight element 424 maydenote one or more torques, thrusts, airspeed velocities, forces,altitudes, groundspeed velocities, directions during flight, directionsfacing, forces, orientations, and the like thereof. For example, andwithout limitation, flight element 424 may denote that aircraft iscruising at an altitude and/or with a sufficient magnitude of forwardthrust. As a further non-limiting example, flight status may denote thatis building thrust and/or groundspeed velocity in preparation for atakeoff. As a further non-limiting example, flight element 424 maydenote that aircraft is following a flight path accurately and/orsufficiently.

Still referring to FIG. 4, flight controller 404 may include a chipsetcomponent 428. As used in this disclosure a “chipset component” is acomponent that manages data flow. In an embodiment, and withoutlimitation, chipset component 428 may include a northbridge data flowpath, wherein the northbridge dataflow path may manage data flow fromlogic component 420 to a high-speed device and/or component, such as aRAM, graphics controller, and the like thereof. In another embodiment,and without limitation, chipset component 428 may include a southbridgedata flow path, wherein the southbridge dataflow path may manage dataflow from logic component 420 to lower-speed peripheral buses, such as aperipheral component interconnect (PCI), industry standard architecture(ICA), and the like thereof. In an embodiment, and without limitation,southbridge data flow path may include managing data flow betweenperipheral connections such as ethernet, USB, audio devices, and thelike thereof. Additionally or alternatively, chipset component 428 maymanage data flow between logic component 420, memory cache, and a flightcomponent 432. As used in this disclosure a “flight component” is aportion of an aircraft that can be moved or adjusted to affect one ormore flight elements. For example, flight component 432 may include acomponent used to affect the aircrafts' roll and pitch which maycomprise one or more ailerons. As a further example, flight component432 may include a rudder to control yaw of an aircraft. In anembodiment, chipset component 428 may be configured to communicate witha plurality of flight components as a function of flight element 424.For example, and without limitation, chipset component 428 may transmitto an aircraft rotor to reduce torque of a first lift propulsor andincrease the forward thrust produced by a pusher component to perform aflight maneuver.

In an embodiment, and still referring to FIG. 4, flight controller 404may be configured generate an autonomous function. As used in thisdisclosure an “autonomous function” is a mode and/or function of flightcontroller 404 that controls aircraft automatically. For example, andwithout limitation, autonomous function may perform one or more aircraftmaneuvers, take offs, landings, altitude adjustments, flight levelingadjustments, turns, climbs, and/or descents. As a further non-limitingexample, autonomous function may adjust one or more airspeed velocities,thrusts, torques, and/or groundspeed velocities. As a furthernon-limiting example, autonomous function may perform one or more flightpath corrections and/or flight path modifications as a function offlight element 424. In an embodiment, autonomous function may includeone or more modes of autonomy such as, but not limited to, autonomousmode, semi-autonomous mode, and/or non-autonomous mode. As used in thisdisclosure “autonomous mode” is a mode that automatically adjusts and/orcontrols aircraft and/or the maneuvers of aircraft in its entirety. Forexample, autonomous mode may denote that flight controller 404 willadjust the aircraft. As used in this disclosure a “semi-autonomous mode”is a mode that automatically adjusts and/or controls a portion and/orsection of aircraft. For example, and without limitation,semi-autonomous mode may denote that a pilot will control thepropulsors, wherein flight controller 404 will control the aileronsand/or rudders. As used in this disclosure “non-autonomous mode” is amode that denotes a pilot will control aircraft and/or maneuvers ofaircraft in its entirety.

In an embodiment, and still referring to FIG. 4, flight controller 404may generate autonomous function as a function of an autonomousmachine-learning model. As used in this disclosure an “autonomousmachine-learning model” is a machine-learning model to produce anautonomous function output given flight element 424 and a pilot signal436 as inputs; this is in contrast to a non-machine learning softwareprogram where the commands to be executed are determined in advance by auser and written in a programming language. As used in this disclosure a“pilot signal” is an element of datum representing one or more functionsa pilot is controlling and/or adjusting. For example, pilot signal 436may denote that a pilot is controlling and/or maneuvering ailerons,wherein the pilot is not in control of the rudders and/or propulsors. Inan embodiment, pilot signal 436 may include an implicit signal and/or anexplicit signal. For example, and without limitation, pilot signal 436may include an explicit signal, wherein the pilot explicitly statesthere is a lack of control and/or desire for autonomous function. As afurther non-limiting example, pilot signal 436 may include an explicitsignal directing flight controller 404 to control and/or maintain aportion of aircraft, a portion of the flight plan, the entire aircraft,and/or the entire flight plan. As a further non-limiting example, pilotsignal 436 may include an implicit signal, wherein flight controller 404detects a lack of control such as by a malfunction, torque alteration,flight path deviation, and the like thereof. In an embodiment, andwithout limitation, pilot signal 436 may include one or more explicitsignals to reduce torque, and/or one or more implicit signals thattorque may be reduced due to reduction of airspeed velocity. In anembodiment, and without limitation, pilot signal 436 may include one ormore local and/or global signals. For example, and without limitation,pilot signal 436 may include a local signal that is transmitted by apilot and/or crew member. As a further non-limiting example, pilotsignal 436 may include a global signal that is transmitted by airtraffic control and/or one or more remote users that are incommunication with the pilot of aircraft. In an embodiment, pilot signal436 may be received as a function of a tri-state bus and/or multiplexorthat denotes an explicit pilot signal should be transmitted prior to anyimplicit or global pilot signal.

Still referring to FIG. 4, autonomous machine-learning model may includeone or more autonomous machine-learning processes such as supervised,unsupervised, or reinforcement machine-learning processes that flightcontroller 404 and/or a remote device may or may not use in thegeneration of autonomous function. As used in this disclosure “remotedevice” is an external device to flight controller 404. Additionally oralternatively, autonomous machine-learning model may include one or moreautonomous machine-learning processes that a field-programmable gatearray (FPGA) may or may not use in the generation of autonomousfunction. Autonomous machine-learning process may include, withoutlimitation machine learning processes such as simple linear regression,multiple linear regression, polynomial regression, support vectorregression, ridge regression, lasso regression, elasticnet regression,decision tree regression, random forest regression, logistic regression,logistic classification, K-nearest neighbors, support vector machines,kernel support vector machines, naïve bayes, decision treeclassification, random forest classification, K-means clustering,hierarchical clustering, dimensionality reduction, principal componentanalysis, linear discriminant analysis, kernel principal componentanalysis, Q-learning, State Action Reward State Action (SARSA), Deep-Qnetwork, Markov decision processes, Deep Deterministic Policy Gradient(DDPG), or the like thereof.

In an embodiment, and still referring to FIG. 4, autonomous machinelearning model may be trained as a function of autonomous training data,wherein autonomous training data may correlate a flight element, pilotsignal, and/or simulation data to an autonomous function. For example,and without limitation, a flight element of an airspeed velocity, apilot signal of limited and/or no control of propulsors, and asimulation data of required airspeed velocity to reach the destinationmay result in an autonomous function that includes a semi-autonomousmode to increase thrust of the propulsors. Autonomous training data maybe received as a function of user-entered valuations of flight elements,pilot signals, simulation data, and/or autonomous functions. Flightcontroller 404 may receive autonomous training data by receivingcorrelations of flight element, pilot signal, and/or simulation data toan autonomous function that were previously received and/or determinedduring a previous iteration of generation of autonomous function.Autonomous training data may be received by one or more remote devicesand/or FPGAs that at least correlate a flight element, pilot signal,and/or simulation data to an autonomous function. Autonomous trainingdata may be received in the form of one or more user-enteredcorrelations of a flight element, pilot signal, and/or simulation datato an autonomous function.

Still referring to FIG. 4, flight controller 404 may receive autonomousmachine-learning model from a remote device and/or FPGA that utilizesone or more autonomous machine learning processes, wherein a remotedevice and an FPGA is described above in detail. For example, andwithout limitation, a remote device may include a computing device,external device, processor, FPGA, microprocessor and the like thereof.Remote device and/or FPGA may perform the autonomous machine-learningprocess using autonomous training data to generate autonomous functionand transmit the output to flight controller 404. Remote device and/orFPGA may transmit a signal, bit, datum, or parameter to flightcontroller 404 that at least relates to autonomous function.Additionally or alternatively, the remote device and/or FPGA may providean updated machine-learning model. For example, and without limitation,an updated machine-learning model may be comprised of a firmware update,a software update, an autonomous machine-learning process correction,and the like thereof. As a non-limiting example a software update mayincorporate a new simulation data that relates to a modified flightelement. Additionally or alternatively, the updated machine learningmodel may be transmitted to the remote device and/or FPGA, wherein theremote device and/or FPGA may replace the autonomous machine-learningmodel with the updated machine-learning model and generate theautonomous function as a function of the flight element, pilot signal,and/or simulation data using the updated machine-learning model. Theupdated machine-learning model may be transmitted by the remote deviceand/or FPGA and received by flight controller 404 as a software update,firmware update, or corrected autonomous machine-learning model. Forexample, and without limitation autonomous machine learning model mayutilize a neural net machine-learning process, wherein the updatedmachine-learning model may incorporate a gradient boostingmachine-learning process.

Still referring to FIG. 4, flight controller 404 may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Further, flight controller may communicate withone or more additional devices as described below in further detail viaa network interface device. The network interface device may be utilizedfor commutatively connecting a flight controller to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. The network may include anynetwork topology and can may employ a wired and/or a wireless mode ofcommunication.

In an embodiment, and still referring to FIG. 4, flight controller 404may include, but is not limited to, for example, a cluster of flightcontrollers in a first location and a second flight controller orcluster of flight controllers in a second location. Flight controller404 may include one or more flight controllers dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. Flight controller 404 may be configured to distribute one or morecomputing tasks as described below across a plurality of flightcontrollers, which may operate in parallel, in series, redundantly, orin any other manner used for distribution of tasks or memory betweencomputing devices. For example, and without limitation, flightcontroller 404 may implement a control algorithm to distribute and/orcommand the plurality of flight controllers. As used in this disclosurea “control algorithm” is a finite sequence of well-defined computerimplementable instructions that may determine the flight component ofthe plurality of flight components to be adjusted. For example, andwithout limitation, control algorithm may include one or more algorithmsthat reduce and/or prevent aviation asymmetry. As a further non-limitingexample, control algorithms may include one or more models generated asa function of a software including, but not limited to Simulink byMathWorks, Natick, Mass., USA. In an embodiment, and without limitation,control algorithm may be configured to generate an auto-code, wherein an“auto-code,” is used herein, is a code and/or algorithm that isgenerated as a function of the one or more models and/or software's. Inanother embodiment, control algorithm may be configured to produce asegmented control algorithm. As used in this disclosure a “segmentedcontrol algorithm” is control algorithm that has been separated and/orparsed into discrete sections. For example, and without limitation,segmented control algorithm may parse control algorithm into two or moresegments, wherein each segment of control algorithm may be performed byone or more flight controllers operating on distinct flight components.

In an embodiment, and still referring to FIG. 4, control algorithm maybe configured to determine a segmentation boundary as a function ofsegmented control algorithm. As used in this disclosure a “segmentationboundary” is a limit and/or delineation associated with the segments ofthe segmented control algorithm. For example, and without limitation,segmentation boundary may denote that a segment in the control algorithmhas a first starting section and/or a first ending section. As a furthernon-limiting example, segmentation boundary may include one or moreboundaries associated with an ability of flight component 432. In anembodiment, control algorithm may be configured to create an optimizedsignal communication as a function of segmentation boundary. Forexample, and without limitation, optimized signal communication mayinclude identifying the discrete timing required to transmit and/orreceive the one or more segmentation boundaries. In an embodiment, andwithout limitation, creating optimized signal communication furthercomprises separating a plurality of signal codes across the plurality offlight controllers. For example, and without limitation the plurality offlight controllers may include one or more formal networks, whereinformal networks transmit data along an authority chain and/or arelimited to task-related communications. As a further non-limitingexample, communication network may include informal networks, whereininformal networks transmit data in any direction. In an embodiment, andwithout limitation, the plurality of flight controllers may include achain path, wherein a “chain path,” as used herein, is a linearcommunication path comprising a hierarchy that data may flow through. Inan embodiment, and without limitation, the plurality of flightcontrollers may include an all-channel path, wherein an “all-channelpath,” as used herein, is a communication path that is not restricted toa particular direction. For example, and without limitation, data may betransmitted upward, downward, laterally, and the like thereof. In anembodiment, and without limitation, the plurality of flight controllersmay include one or more neural networks that assign a weighted value toa transmitted datum. For example, and without limitation, a weightedvalue may be assigned as a function of one or more signals denoting thata flight component is malfunctioning and/or in a failure state.

Still referring to FIG. 4, the plurality of flight controllers mayinclude a master bus controller. As used in this disclosure a “masterbus controller” is one or more devices and/or components that areconnected to a bus to initiate a direct memory access transaction,wherein a bus is one or more terminals in a bus architecture. Master buscontroller may communicate using synchronous and/or asynchronous buscontrol protocols. In an embodiment, master bus controller may includeflight controller 404. In another embodiment, master bus controller mayinclude one or more universal asynchronous receiver-transmitters (UART).For example, and without limitation, master bus controller may includeone or more bus architectures that allow a bus to initiate a directmemory access transaction from one or more buses in the busarchitectures. As a further non-limiting example, master bus controllermay include one or more peripheral devices and/or components tocommunicate with another peripheral device and/or component and/or themaster bus controller. In an embodiment, master bus controller may beconfigured to perform bus arbitration. As used in this disclosure “busarbitration” is method and/or scheme to prevent multiple buses fromattempting to communicate with and/or connect to master bus controller.For example and without limitation, bus arbitration may include one ormore schemes such as a small computer interface system, wherein a smallcomputer interface system is a set of standards for physical connectingand transferring data between peripheral devices and master buscontroller by defining commands, protocols, electrical, optical, and/orlogical interfaces. In an embodiment, master bus controller may receiveintermediate representation 412 and/or output language from logiccomponent 420, wherein output language may include one or moreanalog-to-digital conversions, low bit rate transmissions, messageencryptions, digital signals, binary signals, logic signals, analogsignals, and the like thereof described above in detail.

Still referring to FIG. 4, master bus controller may communicate with aslave bus. As used in this disclosure a “slave bus” is one or moreperipheral devices and/or components that initiate a bus transfer. Forexample, and without limitation, slave bus may receive one or morecontrols and/or asymmetric communications from master bus controller,wherein slave bus transfers data stored to master bus controller. In anembodiment, and without limitation, slave bus may include one or moreinternal buses, such as but not limited to a/an internal data bus,memory bus, system bus, front-side bus, and the like thereof. In anotherembodiment, and without limitation, slave bus may include one or moreexternal buses such as external flight controllers, external computers,remote devices, printers, aircraft computer systems, flight controlsystems, and the like thereof.

In an embodiment, and still referring to FIG. 4, control algorithm mayoptimize signal communication as a function of determining one or morediscrete timings. For example, and without limitation master buscontroller may synchronize timing of the segmented control algorithm byinjecting high priority timing signals on a bus of the master buscontrol. As used in this disclosure a “high priority timing signal” isinformation denoting that the information is important. For example, andwithout limitation, high priority timing signal may denote that asection of control algorithm is of high priority and should be analyzedand/or transmitted prior to any other sections being analyzed and/ortransmitted. In an embodiment, high priority timing signal may includeone or more priority packets. As used in this disclosure a “prioritypacket” is a formatted unit of data that is communicated between theplurality of flight controllers. For example, and without limitation,priority packet may denote that a section of control algorithm should beused and/or is of greater priority than other sections.

Still referring to FIG. 4, flight controller 404 may also be implementedusing a “shared nothing” architecture in which data is cached at theworker, in an embodiment, this may enable scalability of aircraft and/orcomputing device. Flight controller 404 may include a distributer flightcontroller. As used in this disclosure a “distributer flight controller”is a component that adjusts and/or controls a plurality of flightcomponents as a function of a plurality of flight controllers. Forexample, distributer flight controller may include a flight controllerthat communicates with a plurality of additional flight controllersand/or clusters of flight controllers. In an embodiment, distributedflight control may include one or more neural networks. For example,neural network also known as an artificial neural network, is a networkof “nodes,” or data structures having one or more inputs, one or moreoutputs, and a function determining outputs based on inputs. Such nodesmay be organized in a network, such as without limitation aconvolutional neural network, including an input layer of nodes, one ormore intermediate layers, and an output layer of nodes. Connectionsbetween nodes may be created via the process of “training” the network,in which elements from a training dataset are applied to the inputnodes, a suitable training algorithm (such as Levenberg-Marquardt,conjugate gradient, simulated annealing, or other algorithms) is thenused to adjust the connections and weights between nodes in adjacentlayers of the neural network to produce the desired values at the outputnodes. This process is sometimes referred to as deep learning.

Still referring to FIG. 4, a node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above. In anembodiment, and without limitation, a neural network may receivesemantic units as inputs and output vectors representing such semanticunits according to weights w_(i) that are derived using machine-learningprocesses as described in this disclosure.

Still referring to FIG. 4, flight controller may include asub-controller 440. As used in this disclosure a “sub-controller” is acontroller and/or component that is part of a distributed controller asdescribed above; for instance, flight controller 404 may be and/orinclude a distributed flight controller made up of one or moresub-controllers. For example, and without limitation, sub-controller 440may include any controllers and/or components thereof that are similarto distributed flight controller and/or flight controller as describedabove. Sub-controller 440 may include any component of any flightcontroller as described above. Sub-controller 440 may be implemented inany manner suitable for implementation of a flight controller asdescribed above. As a further non-limiting example, sub-controller 440may include one or more processors, logic components and/or computingdevices capable of receiving, processing, and/or transmitting dataacross the distributed flight controller as described above. As afurther non-limiting example, sub-controller 440 may include acontroller that receives a signal from a first flight controller and/orfirst distributed flight controller component and transmits the signalto a plurality of additional sub-controllers and/or flight components.

Still referring to FIG. 4, flight controller may include a co-controller444. As used in this disclosure a “co-controller” is a controller and/orcomponent that joins flight controller 404 as components and/or nodes ofa distributer flight controller as described above. For example, andwithout limitation, co-controller 444 may include one or morecontrollers and/or components that are similar to flight controller 404.As a further non-limiting example, co-controller 444 may include anycontroller and/or component that joins flight controller 404 todistributer flight controller. As a further non-limiting example,co-controller 444 may include one or more processors, logic componentsand/or computing devices capable of receiving, processing, and/ortransmitting data to and/or from flight controller 404 to distributedflight control system. Co-controller 444 may include any component ofany flight controller as described above. Co-controller 444 may beimplemented in any manner suitable for implementation of a flightcontroller as described above.

In an embodiment, and with continued reference to FIG. 4, flightcontroller 404 may be designed and/or configured to perform any method,method step, or sequence of method steps in any embodiment described inthis disclosure, in any order and with any degree of repetition. Forinstance, flight controller 404 may be configured to perform a singlestep or sequence repeatedly until a desired or commanded outcome isachieved; repetition of a step or a sequence of steps may be performediteratively and/or recursively using outputs of previous repetitions asinputs to subsequent repetitions, aggregating inputs and/or outputs ofrepetitions to produce an aggregate result, reduction or decrement ofone or more variables such as global variables, and/or division of alarger processing task into a set of iteratively addressed smallerprocessing tasks. Flight controller may perform any step or sequence ofsteps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like; division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Referring now to FIG. 5, an exemplary embodiment of a machine-learningmodule 500 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 504 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 508 given data provided as inputs 512;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 5, “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 504 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 504 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 504 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 504 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 504 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 504 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data504 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 5,training data 504 may include one or more elements that are notcategorized; that is, training data 504 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 504 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 504 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 504 used by machine-learning module 500 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample flight elements and/or pilot signals may be inputs, wherein anoutput may be an autonomous function.

Further referring to FIG. 5, training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 516. Training data classifier 516 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 500 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 504. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naïve Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 416 may classify elements of training data tosub-categories of flight elements such as torques, forces, thrusts,directions, and the like thereof.

Still referring to FIG. 5, machine-learning module 500 may be configuredto perform a lazy-learning process 520 and/or protocol, which mayalternatively be referred to as a “lazy loading” or “call-when-needed”process and/or protocol, may be a process whereby machine learning isconducted upon receipt of an input to be converted to an output, bycombining the input and training set to derive the algorithm to be usedto produce the output on demand. For instance, an initial set ofsimulations may be performed to cover an initial heuristic and/or “firstguess” at an output and/or relationship. As a non-limiting example, aninitial heuristic may include a ranking of associations between inputsand elements of training data 504. Heuristic may include selecting somenumber of highest-ranking associations and/or training data 504elements. Lazy learning may implement any suitable lazy learningalgorithm, including without limitation a K-nearest neighbors algorithm,a lazy naïve Bayes algorithm, or the like; persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variouslazy-learning algorithms that may be applied to generate outputs asdescribed in this disclosure, including without limitation lazy learningapplications of machine-learning algorithms as described in furtherdetail below.

Alternatively or additionally, and with continued reference to FIG. 5,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 524. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 524 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 524 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 504set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 5, machine-learning algorithms may include atleast a supervised machine-learning process 528. At least a supervisedmachine-learning process 528, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude flight elements and/or pilot signals as described above asinputs, autonomous functions as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweeninputs and outputs; scoring function may, for instance, seek to maximizethe probability that a given input and/or combination of elements inputsis associated with a given output to minimize the probability that agiven input is not associated with a given output. Scoring function maybe expressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data 504. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of at least a supervised machine-learning process528 that may be used to determine relation between inputs and outputs.Supervised machine-learning processes may include classificationalgorithms as defined above.

Further referring to FIG. 5, machine learning processes may include atleast an unsupervised machine-learning processes 532. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 5, machine-learning module 500 may be designedand configured to create a machine-learning model 524 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 5, machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Referring now to FIG. 6, an exemplary method 600 of orienting a thrustpropulsor in response to a failure event of a vertical take-off andlanding (VTOL) aircraft is illustrated by way of a flow diagram. At step605, a plurality of lift propulsors mechanically connected to a VTOLaircraft produce lift. Lift propulsor includes any lift propulsordescribed in this disclosure, including with reference to FIGS. 1-5.VTOL aircraft includes any VTOL aircraft described in this disclosure,including with reference to FIGS. 1-5.

With continued reference to FIG. 6, at step 610, at least a sensor of aplurality of sensors detects a failure of at least a lift propulsor ofplurality of lift propulsors. Sensor includes any sensor described inthis disclosure, including with reference to FIGS. 1-5. Failure includesany failure described in this disclosure, including with reference toFIGS. 1-5. In some embodiments, failure of at least a lift propulsor mayintroduce a yaw moment. In some embodiments, failure of at least a liftpropulsor may prevent flight controller from using the plurality of liftpropulsors to control yaw. In some embodiments, failure of at least alift propulsor may degrade lift provided by the at least a liftpropulsor. In some embodiments, step 610 may additionally includedetecting, using at least a sensor, rotation of at least a liftpropulsor.

With continued reference to FIG. 6, at step 615, at least a sensortransmits a failure datum in response to the failure. Failure datumincludes any failure datum described in this disclosure, including withreference to FIGS. 1-5. In some embodiments, failure datum may includeat least an element of data describing failure of at least a liftpropulsor.

With continued reference to FIG. 6, at step 620, a thrust propulsormechanically attached to VTOL aircraft with an orientable joint producesthrust. Thrust propulsor includes any thrust propulsor described in thisdisclosure, including with reference to FIGS. 1-5. Orientable jointincludes any orientable joint described in this disclosure, includingwith reference to FIGS. 1-5.

With continued reference to FIG. 6, at step 625, a flight controllercommunicative with plurality of sensors and orientable joint receivesfailure datum. Flight controller includes any flight controllerdescribed in this disclosure, including with reference to FIGS. 1-5.

With continued reference to FIG. 6, at step 630, flight controllergenerates a thrust orientation datum as a function of failure datum.Thrust orientation datum includes any thrust orientation datum describedin this disclosure, including with reference to FIGS. 1-5. In someembodiments, thrust orientation datum may include a rotation andorienting thrust propulsor may include rotating, using orientable joint,the thrust propulsor. In some embodiments, thrust orientation datum mayinclude a translation and orienting thrust propulsor may includetranslating, using orientable joint, the thrust propulsor.

With continued reference to FIG. 6, at step 635, flight controllertransmits thrust orientation datum to orientable joint.

With continued reference to FIG. 6, at step 640, orientable jointreceives thrust orientation datum. In some embodiments, orientable jointmay include a gimbal.

With continued reference to FIG. 6, at step 645, orientable jointorients thrust propulsor as a function of thrust orientation datum. Insome embodiments, orienting thrust propulsor may introduce a yaw momentconfigured to control yaw of VTOL aircraft.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random-access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 7 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 700 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 700 includes a processor 704 and a memory708 that communicate with each other, and with other components, via abus 712. Bus 712 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 704 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 704 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 704 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating-pointunit (FPU), and/or system on a chip (SoC).

Memory 708 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 716 (BIOS), including basic routines that help totransfer information between elements within computer system 700, suchas during start-up, may be stored in memory 708. Memory 708 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 720 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 708 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 700 may also include a storage device 724. Examples of astorage device (e.g., storage device 724) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 724 may be connected to bus 712 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 724 (or one or morecomponents thereof) may be removably interfaced with computer system 700(e.g., via an external port connector (not shown)). Particularly,storage device 724 and an associated machine-readable medium 728 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 700. In one example, software 720 may reside, completelyor partially, within machine-readable medium 728. In another example,software 720 may reside, completely or partially, within processor 704.

Computer system 700 may also include an input device 732. In oneexample, a user of computer system 700 may enter commands and/or otherinformation into computer system 700 via input device 732. Examples ofan input device 732 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 732may be interfaced to bus 712 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 712, and any combinations thereof. Input device 732 mayinclude a touch screen interface that may be a part of or separate fromdisplay 736, discussed further below. Input device 732 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 700 via storage device 724 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 740. A network interfacedevice, such as network interface device 740, may be utilized forconnecting computer system 700 to one or more of a variety of networks,such as network 744, and one or more remote devices 748 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 744,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 720,etc.) may be communicated to and/or from computer system 700 via networkinterface device 740.

Computer system 700 may further include a video display adapter 752 forcommunicating a displayable image to a display device, such as displaydevice 736. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 752 and display device 736 may be utilized incombination with processor 704 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 700 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 712 via a peripheral interface 756. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for orienting a thrust propulsor inresponse to a failure event of a vertical take-off and landing aircraft,the system comprising: a plurality of lift propulsors mechanicallyconnected to a vertical take-off and landing (VTOL) aircraft, whereineach of the plurality of lift propulsors are configured to produce liftalong a single direction vector substantially parallel to a yaw axis,the single direction vector comprising an aggregate of a plurality offixed direction vectors, wherein each lift propulsor produces lift alonga corresponding fixed direction vector; a thrust propulsor mechanicallyattached to the VTOL aircraft with an orientable joint, wherein thethrust propulsor is configured to produce forward thrust; and a flightcontroller communicative with a plurality of sensors and the orientablejoint, wherein the flight controller is configured to: generate a thrustorientation datum; and transmit the thrust orientation datum to theorientable joint; wherein the orientable joint is configured to: receivethe thrust orientation datum; and orient an angle of the thrustpropulsor about the yaw axis as a function of the thrust orientationdatum.
 2. The system of claim 1, wherein the thrust orientation datumrepresents a rotation; and the orientable joint is configured to rotatethe thrust propulsor.
 3. The system of claim 1, wherein the thrustorientation datum represents a translation; and the orientable joint isconfigured to translate the thrust propulsor.
 4. The system of claim 1,wherein a failure of a lift propulsor of the plurality of liftpropulsors introduces a yaw moment.
 5. The system of claim 1, wherein afailure of a lift propulsor of the plurality of lift propulsors preventsthe flight controller from using the plurality of lift propulsors tocontrol yaw.
 6. The system of claim 1, wherein orienting the thrustpropulsor introduces a yaw moment configured to control yaw of the VTOLaircraft.
 7. The system of claim 1, wherein the orientable joint is agimbal.
 8. The system of claim 1, further comprising: a plurality ofsensors, wherein at least a sensor of the plurality of sensors isconfigured to: detect a failure of a lift propulsor of the plurality oflift propulsors; and transmit a failure datum in response to thefailure; and wherein the failure of a lift propulsor degrades liftprovided by the plurality of lift propulsors.
 9. The system of claim 8,wherein the at least a sensor of the plurality of sensors comprises atleast a rotation sensor configured to detect rotation of the at least alift propulsor.
 10. The system of claim 8, wherein the failure datumcomprises at least an element of data describing the failure of the atleast a lift propulsor.
 11. A method of orienting a thrust propulsor inresponse to a failure event of a vertical take-off and landing aircraft,the method comprising: producing, a plurality of lift propulsorsmechanically connected to a vertical take-off and landing (VTOL)aircraft, a lift along a single direction vector substantially parallelto a yaw axis, the single direction vector comprising an aggregate of aplurality of fixed direction vectors, wherein each lift propulsorproduces lift along a corresponding fixed direction vector; producing,using a thrust propulsor mechanically attached to the VTOL aircraft withan orientable joint, a forward thrust; receiving, using a flightcontroller communicative with a plurality of sensors and the orientablejoint, a failure datum; generating, using the flight controller, athrust orientation datum; transmitting, using the flight controller, thethrust orientation datum to the orientable joint; receiving, using theorientable joint, the thrust orientation datum; and orienting, using theorientable joint, an angle of the thrust propulsor about the yaw axis asa function of the thrust orientation datum.
 12. The method of claim 11,wherein the thrust orientation datum represents a rotation; andorienting the thrust propulsor comprises rotating, using the orientablejoint, a thrust propulsor.
 13. The method of claim 11, wherein thethrust orientation datum represents a translation; and orienting thethrust propulsor comprises translating, using the orientable joint, athrust propulsor.
 14. The method of claim 11, wherein a failure of alift propulsor of the plurality of lift propulsors introduces a yawmoment.
 15. The method of claim 11, wherein a failure of a liftpropulsor of the plurality of lift propulsors prevents the flightcontroller from using the plurality of lift propulsors to control yaw.16. The method of claim 11, wherein orienting the thrust propulsorintroduces a yaw moment configured to control yaw of the VTOL aircraft.17. The method of claim 11, wherein the orientable joint is a gimbal.18. The method of claim 11, further comprising: detecting, using atleast a sensor of a plurality of sensors, a failure of a lift propulsorof the plurality of lift propulsors; transmitting, using the at least asensor, the failure datum in response to a failure; and wherein thefailure of a lift propulsor degrades lift provided by the plurality oflift propulsors.
 19. The method of claim 18, wherein detecting thefailure comprises detecting, using at least a sensor, rotation of atleast a lift propulsor.
 20. The method of claim 18, wherein the failuredatum comprises at least an element of data describing the failure ofthe at least a lift propulsor.